Socially-aware traffic management utilizes social information to optimize traffic management in the Internet in terms of traffic load, energy consumption, or end user satisfaction. Several use cases can benefit from socially-aware traffic management and the performance of overlay applications can be enhanced. We present existing use cases and their socially-aware approaches and solutions, but also raise discussions on additional benefits from the integration of social information into traffic management as well as practical aspects in this domain.

Enterprise social software tools are increasingly being used to support the communication and collaboration between employees, as well as to facilitate the collaborative organisation of information and knowledge within companies. Not only do these tools help to develop and maintain an efficient social organisation, they also produce massive amounts of fine-grained data on collaborations, communication and other forms of social relationships within an enterprise. In this chapter, we argue that the availability of these data provides unique opportunities to monitor and analyse social structures and their impact on the success and performance of individuals, teams, communities and organisations. We further review methods from the planning, design and optimisation of telecommunication networks and discuss challenges arising when wanting to apply them to optimise the structure of enterprise social networks.

The fast growth of video streaming is responsible for a huge amount of traffic over the past few years. Due to the variety and popularity of video content on the Internet, a potential market is emerging for video providers, but also poses challenges for network operators in order to meet users' expectations. To address this problem, network operators need a mechanism to monitor the video quality and Quality of Experience (QoE) as perceived by the users. This allows reacting on quality degradations to improve the service in the network. With the paradigm of Network Function Virtualization, network operators are able to deploy such a Virtual Network Function (VNF) for video monitoring in the cloud. In this work, we investigate the feasibility of deploying a VNF for video buffer monitoring in the Amazon Web Service (AWS) cloud. To this end, we implement the VNF to analyze video flows in the network by using deep packet inspection. We investigate the influence of different points of presence (PoP) and a high mobility environment on the accuracy of the VNF for monitoring the video buffer and QoE perceived by the users. Our findings show that the accuracy of the VNF for video buffer monitoring decreases with the distance of the PoP to the client. This is not only due to the delay and bottleneck between the monitoring point and the client, but also due to the high mobility client access network. This means with increasing distance of the PoP to the client in a mobility environment, the probability of detecting stalling events also decreases, which is key factor to evaluate the QoE for video streaming.

Software Defined Networking (SDN) has emerged as a promising networking paradigm overcoming various drawbacks of current communication networks. The control and data plane of switching devices is decoupled and control functions are centralized at the network controller. In SDN, each new flow introduces additional signaling traffic between the switch and the controller. Based on this traffic, rules are created in the flow table of the switch, which specify the forwarding behavior. To avoid table overflows, unused entries are removed after a predefined time-out period. Given a specific traffic mix, the choice of this time-out period affects the trade-off between signaling rate and table occupancy. As a result, network operators have to adjust this parameter to enable a smooth and efficient network operation. Due to the complexity of this problem caused by the various traffic flows in a network, a suitable abstraction is necessary in order to derive valid parameter values in time. The contribution of this work is threefold. Firstly, we formulate a simple analytical model that allows optimizing the network performance with respect to the table occupancy and the signaling rate. Secondly, we validate the model by means of simulation. Thirdly, we illustrate the impact of the time-out period on the signaling traffic and the flow table occupancy for different data-plane traffic mixes and characteristics. This includes scenarios with single application instances, as well as multiple application instances of different application types in an SDN-enabled network.

One of the benefits when network operators adopt the Software Defined Networking (SDN) paradigm is the ability to monitor the traffic in the network without an additional network management system. Usually, SDN controllers utilize OpenFlow statistics messages in order to regularly gather information about all flows in the network. However, using the same polling interval for all flows does not take into account the heterogeneity of real world traffic and thus results in an imbalance between monitoring accuracy and control plane overhead. In particular, frequent querying results in a high resource consumption at the controller. This work proposes a Selective Flow Monitoring (SFM) mechanism that allows administrators to classify flows according to their individual requirements in terms of monitoring frequency, e.g., less frequent polling of elephant flows and frequent polling of QoS sensitive VoIP connections. We compare the performance of the SFM mechanism with the default monitoring scheme in a testbed featuring the Open Network Operating System (ONOS) controller. In this context, the CPU utilization of the controller is used as performance indicator. After identifying relevant influence factors like the number of flows and switches in the network, we investigate the viability of the approaches in different scenarios. Finally, we provide guidelines regarding their choice.

Offloading mobile Internet data via WiFi has emerged as an omnipresent trend. WiFi networks are already widely deployed by many private and public institutions (e.g., libraries, cafes, restaurants) but also by commercial services to provide alternative Internet access for their customers and to mitigate the load on mobile networks. Moreover, smart cities start to install WiFi infrastructure for current and future civic services, e.g., based on sensor networks or the Internet of Things. A simple model for the distribution of WiFi hotspots in an urban environment is presented. The hotspot locations are modeled with a uniform distribution of the angle and an exponential distribution of the distance, which is truncated to the city limits. We compare the characteristics of this model in detail to the real distributions. Moreover, we show the applicability and the limitations of this model, and the results suggest that the model can be used in scenarios, which do not require an accurate spatial collocation of the hotspots, such as offloading potential, coverage, or signal strength.